IS300 Exam #3 Guide

Management Information Systems IS 300: Business Analytics Overview

Presentation and Accessibility

  • The presentation conforms to a UMBC PowerPoint Template aimed at universal accessibility.
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Learning Objectives

  • Understand the history of decision-making, its phases, associated risks, and Simon’s Intelligence, Decision, Choice theory.
  • Explain the decision support framework and how technology assists managers during the decision-making process.
  • Describe the various phases of the business analytics process.
  • Provide full definitions and examples of descriptive analytics, predictive analytics, and prescriptive analytics.

Bounded Rationality

  • Bounded Rationality: Decision makers experience uncertainty due to cognitive limitations, the complexity of problems, and time constraints when acquiring information.
  • Satisficing: The act of accepting a solution that meets an aspiration level rather than seeking the optimal solution.

Why Managers Need IT Support

  • Decision-making challenges arise from:
      - Increasing number of alternatives
      - Time constraints dictating decision speed
      - Increased uncertainty that necessitates sophisticated analyses
      - Need for rapid access to remote information, expert consultation, and group decision-making sessions.

The Manager’s Job & Decision Making

  • Management Definition: A process by which an organization achieves its goals using resources including people, money, materials, and information.
  • Productivity: Defined as the ratio of inputs to outputs utilized to measure organizational success.
  • Decision Definition: A choice made among two or more alternatives, involving a systematic decision-making process.
  • Three Basic Roles of Managers (Mintzberg, 1973):
      1. Interpersonal Roles: Figurehead, leader, liaison
      2. Informational Roles: Monitor, disseminator, spokesperson, analyzer
      3. Decisional Roles: Entrepreneur, disturbance handler, resource allocator, negotiator

Problem Structure & The Nature of Decisions

  1. Operational Control: Executing specific tasks effectively and efficiently.
  2. Management Control: Efficiently acquiring and using resources to achieve organizational goals.
  3. Strategic Planning: Setting long-range goals and policies for growth and resource allocation.

Phases in Decision Making

  • Definition (Simon): "The process of choosing among alternative courses of action to achieve a goal or set of goals."
Phases:
  1. Intelligence: Discovering, identifying, and understanding problems within the organization.
  2. Design: Identifying possible solutions to the problem at hand.
  3. Choice: Selecting among alternative solutions.
  4. Implementation: Executing the chosen solution and monitoring its effectiveness.

Decision Making & Risk Continuum

  • Completeness of Knowledge vs. Ignorance of Uncertainty: Managers are encouraged to acquire more data when in doubt to inform their decisions better.

The Business Analytics Process

  • Analytics Users:
      1. Business Users: Access analytics for basic reporting.
      2. Business Analysts: Manage, clean, abstract, and aggregate data; run analytical and statistical procedures.
      3. Data Scientists: Build upon business analysts’ skills, focusing on mathematical applications.

Business Analytics Models

  • Business Analytics (BA): Developing actionable decisions based on insights from historical data. BA utilizes various tools for descriptive, predictive, and prescriptive models and communicates these insights to decision makers.
      - Descriptive Analytics: Summarizes past events; provides historical insights into production, financials, operations, sales, etc.
      - Predictive Analytics: Analyzes recent data to find patterns and forecast future outcomes; primarily based on probabilities (e.g., targeted marketing strategies).
      - Prescriptive Analytics: Recommends actions based on models and quantifies potential outcomes; requires predictive analytics as a foundation.

Business Analytics Tools

  • Excel: The most popular BA tool, often integrated with analytics software.
  • Online Analytical Processing (OLAP): Enables multidimensional analysis of data.
  • Data Mining: Extracts valuable business information from large databases by predicting trends and discovering patterns.
  • Decision Support Systems (DSS): Interactively manipulates data to aid in solving unstructured problems.

Decision Support System (DSS)

  • Defined as systems combining models and data to analyze semi-structured problems with significant user engagement.
  • Key Differences Between BI/BA and DSS:
      - BI/BA is a broader business strategy, while DSS focuses on decision-making methodologies.
      - BI/BA is typically vendor-supported, whereas DSS may involve custom functions.

DSS Modeling Techniques

  1. Sensitivity Analysis: Evaluates how changes in input affect output.
  2. Goal-Seeking Analysis: Works backward to determine input needed for a desired output.
  3. What-If Analysis: Assesses the impact of different input changes on a proposed solution.

Presentation Tools: Dashboards

  • Dashboards offer timely access to information and management reports with capabilities such as trend analysis and exception reporting.

Summary

  • Human decision-making is inherently complex; a data-driven approach enhances decision quality.
  • The decision-making process comprises intelligence, design, and choice, culminating in implementation.
  • Business analytics equips organizations with insights derived from historical data, driving better decision-making.

Customer Relationship Management (CRM) & Supply Chain Management

Learning Objectives

  • Identify functions of operational CRM and collaborative CRM strategies.
  • Apply applications of operational CRM components in businesses.
  • Analyze advantages and disadvantages of various CRM systems: mobile, on-demand, open-source, social, and real-time.

Customer Relationship Management Overview

  • CRM is a primary revenue generator focused on maximizing high-value repeat customers while minimizing churn.

Components of CRM

  • Operational CRM Systems: Supports front-office processes directly interacting with customers (sales, marketing, service).
  • Analytical CRM Systems: Provides back-end business intelligence that analyzes customer behavior and enhances relationships across the organization through collaborative systems.

Touchpoints

  • Integration of online and offline channels is referred to as multi-channeling or omni-channeling.

Operational vs. Analytical CRM

Operational CRM ComponentsAnalytical CRM Components
Customer-facing applicationsCustomer data warehouse
Sales, marketing, serviceData mining, decision support, BI, OLAP

Analytical CRM Goals

  • Analyze customer data to aid in:
      - Designing targeted marketing campaigns
      - Increasing acquisition, cross-selling, and upselling
      - Providing input for product and service decisions
      - Financial forecasting and customer profitability analyses.

Customer-facing Applications

  1. Customer Service & Support: Automates service requests and complaints.
  2. Customer Interaction Centers (CIC): Facilitates communication through various channels.
  3. Call Center: Centralized communication for handling requests.
  4. Outbound Telesales: Generates sales call lists.
  5. Inbound Teleservice: Direct communication for order initiation and inquiries.
  6. Information Help Desk: Assists customers with inquiries and processing complaints.
  7. Live Chat: Enables real-time communication between customers and representatives.

Customer Touching Applications

  1. Search & Comparison Capabilities: Allows customers to compare products online.
  2. Technical Information: Provides personalized experiences to enhance loyalty.
  3. Mass Customization: Empowers customers to configure products.
  4. Personalized Web Pages: Retain customer preferences and transaction history.
  5. FAQs: Streamlines responses to common inquiries.
  6. Loyalty Programs: Rewards repeat customers to reinforce brand loyalty.

Open-Source CRM Systems

  • Benefits: Favorable pricing, customization, rapid updates, and extensive support information.
  • Disadvantages: Quality control risks and potential compatibility issues with IT platforms.

Salesforce Automation (SFA)

  • Automatically records all components of the sales transaction process, including contact management and sales lead tracking.
  • Example: SugarCRM, an open-source CRM application.

Campaign Management in CRM

  1. Market Segmentation: Dividing markets into subsets for targeted campaigns based on common needs.
  2. Campaign Planning: Ensures the right messages reach the right audience through appropriate channels.
  3. Campaign Management Tools: Assist organizations in efficient campaign planning and implementation.

Marketing Strategies in CRM

  • CRM systems utilize data mining to create purchasing profiles, increasing the effectiveness of marketing strategies such as cross-selling, upselling, and bundling.

Types of CRM

  1. Social CRM: Leverages social media for customer engagement and relationship building.
  2. On-Demand Systems: Cloud-based systems minimize upfront costs and maintenance needs.
  3. Mobile CRM: Facilitates communications via mobile devices.
  4. Real-Time CRM: Enables instantaneous customer interactions to address their needs.

Supply Chain Management

Supply Chain Essentials

  • Supply Chain Definition: The flow of materials, information, money, and services from suppliers through to customers.
  • Key Aspects: Product development, marketing, operations, distribution, finance, customer service.
  • Supply Chain Visibility: The capacity to track relevant information about materials as they move through the production processes.
  • Inventory Velocity: SCM systems speed up product delivery once materials are received.

Supply Chain Management Activities

  • Activities include scheduling plant operations, reallocating resources, managing inventories, and forecasting demand based on various factors.

Supply Chain Flows

  1. Material Flows: Include the movement of physical products and raw materials.
  2. Reverse Flows: Address the return of unwanted or damaged products.
  3. Information Flows: Concern demand, shipments, orders, and returns.
  4. Financial Flows: Involve payment processing and credit-related transactions.

Five Basic Components of SCM

  1. Planning: Development of metrics to monitor customer demand efficiently.
  2. Sourcing: Selection of suppliers for the goods/services needed for production.
  3. Making: Scheduling necessary activities for production and ensuring quality.
  4. Delivering: Coordination of customer orders and logistics.
  5. Returning: Managing the reverse logistics for returned goods.

Challenges in Supply Chain

  • Primary issues arise from uncertainty and inefficiencies, leading to increased operational costs.
  • Bullwhip Effect: Distortion of product demand information leading to erratic order fluctuations.
  • Just-In-Time (JIT): Strategy for minimizing delays in supply availability.
  • Vendor-Managed Inventory (VMI): Suppliers control inventory management based on shared data.
  • Vertical Integration: Acquiring suppliers for better resource control.

Electronic Data Interchange (EDI)

  • Definition: EDI automates routine transactions like purchase orders using standardized formats.
  • Benefits: Reduces errors and cycle times, enhances customer service, and minimizes paper usage.
  • Drawbacks: May require restructuring business processes to accommodate EDI.

Cloud Computing Overview

  • Definition (NIST): A model that enables on-demand access to computing resources with minimal management effort.
  • Virtualization: Facilitates cloud computing by creating virtual servers on physical hardware.

Cloud Service Models

  1. Software as a Service (SaaS): Subscription-based access to applications.
  2. Platform as a Service (PaaS): Control over application settings in a hosting environment.
  3. Infrastructure as a Service (IaaS): Deploying and running software and applications.
  4. Anything as a Service (XaaS): A broad classification accommodating various service architectures.

Cloud Deployment Models

  • Public: Shared and non-exclusive, but less secure.
  • Private: Exclusive access to proprietary systems.
  • Hybrid: Combination of public and private architectures, offering customization and elasticity.

Social Computing

Learning Objectives
  • Describe Web 2.0 tools and assess the benefits and risks of social commerce for businesses.
  • Explore innovative uses of social networking sites for advertising and market research.
  • Discuss the impact of social computing on enhancing customer service.
Web 2.0 Context
  • Definition: Web 2.0 encompasses technologies and applications enabling shared intelligence and collaboration.
      - Key Trends: Tagging, folksonomies, geotagging, blogging, microblogging, wikis, social networks, and mashups.
Social Graph & Social Capital
  • Social Network: A link of individuals or groups tied together by various associations.
  • Social Graph: A visual representation of connections within a social network.
  • Social Capital: The value derived from connections within networks.
Social Networks: Challenges
  • Issues include fake news, security threats, moderation concerns, cyberbullying, and engagement strategies.
Social Computing in Business
  • Social Commerce: Incorporates social interactions in e-commerce.
  • Collaborative Consumption: Focuses on sharing resources.
  • Social Shopping: Leverages network interactions in the shopping experience.
  • Benefits include improved customer engagement and rapid feedback collection.
Risks of Social Computing
  • Concerns: Content moderation, privacy invasion, data security, employee participation reluctance, and misinformation risks.
Applications Across Domains
  • Social computing enhances marketing, market research, recruitment, employee development, and customer relationship management.